Hash_mAP_test.py

import torch
import torch.optim as optim
import os
import time
import utils.evaluate as evaluate
import models.resnet as resnet

from tqdm import tqdm
from loguru import logger
from data.data_loader import sample_dataloader
from utils import AverageMeter
import models.SEMICON as SEMICON

def valid(query_dataloader, train_dataloader, retrieval_dataloader, code_length, args):
    num_classes, att_size, feat_size = args.num_classes, 1, 2048
    model = SEMICON.semicon(code_length=code_length, num_classes=num_classes, att_size=att_size, feat_size=feat_size,
                            device=args.device, pretrained=True)

    model.to(args.device)
    
    model.load_state_dict(torch.load('./checkpoints/' + args.info + '/' + str(code_length) + '/model.pkl'), strict=False)
    model.eval()
    query_code = torch.load('./checkpoints/' + args.info + '/' + str(code_length) + '/query_code.t')
    query_code = query_code.to(args.device)
    query_dataloader.dataset.get_onehot_targets = torch.load('./checkpoints/' + args.info + '/' + str(code_length) + '/query_targets.t')
    B = torch.load('./checkpoints/' + args.info + '/' + str(code_length) + '/database_code.t')
    B = B.to(args.device)
    retrieval_targets = torch.load('./checkpoints/' + args.info + '/' + str(code_length) + '/database_targets.t')
    retrieval_targets = retrieval_targets.to(args.device)

    
    mAP = evaluate.mean_average_precision(
        query_code.to(args.device),
        B,
        query_dataloader.dataset.get_onehot_targets().to(args.device),
        retrieval_targets,
        args.device,
        args.topk,
    )
    print("Code_Length: " + str(code_length), end="; ")
    print('[mAP:{:.5f}]'.format(mAP))